Data augmentation is used widely and includes numerous techniques for improving the generalization ability of deep networks by transforming the training data in ways that do not change the associated label.
A substantial amount of work has gone into the topic of data augmentation and finding automated ways to find the optimal amount and types of data augmentation for training a network on a specific dataset [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
However, the previous methods assume that the amount of data augmentation used is constant from the start to the end of training.
